Overview

Dataset statistics

Number of variables14
Number of observations52704
Missing cells7649
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric13

Alerts

Power (kW) is highly overall correlated with Rear bearing temperature (°C) and 6 other fieldsHigh correlation
Wind direction (°) is highly overall correlated with Nacelle position (°)High correlation
Nacelle position (°) is highly overall correlated with Wind direction (°)High correlation
Rear bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Rotor speed (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Generator RPM (RPM) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Front bearing temperature (°C) is highly overall correlated with Power (kW) and 5 other fieldsHigh correlation
Tower Acceleration X (mm/ss) is highly overall correlated with Power (kW) and 4 other fieldsHigh correlation
Wind speed (m/s) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
Tower Acceleration y (mm/ss) is highly overall correlated with Power (kW) and 6 other fieldsHigh correlation
blade_angle has 705 (1.3%) missing valuesMissing
Rear bearing temperature (°C) has 705 (1.3%) missing valuesMissing
Nacelle ambient temperature (°C) has 706 (1.3%) missing valuesMissing
Front bearing temperature (°C) has 705 (1.3%) missing valuesMissing
Tower Acceleration X (mm/ss) has 706 (1.3%) missing valuesMissing
Tower Acceleration y (mm/ss) has 706 (1.3%) missing valuesMissing
Metal particle count counter has 706 (1.3%) missing valuesMissing
# Date and time has unique valuesUnique
blade_angle has 23179 (44.0%) zerosZeros
Rotor speed (RPM) has 1339 (2.5%) zerosZeros

Reproduction

Analysis started2023-07-08 11:57:45.236433
Analysis finished2023-07-08 11:58:02.600743
Duration17.36 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct52704
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size411.9 KiB
Minimum2020-01-01 00:00:00
Maximum2020-12-31 23:50:00
2023-07-08T17:28:02.656228image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:02.749686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Power (kW)
Real number (ℝ)

Distinct52230
Distinct (%)> 99.9%
Missing451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean786.26408
Minimum-14.492521
Maximum2081.1495
Zeros2
Zeros (%)< 0.1%
Negative4764
Negative (%)9.0%
Memory size411.9 KiB
2023-07-08T17:28:02.854498image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-14.492521
5-th percentile-1.3883804
Q1172.39488
median564.83969
Q31358.9105
95-th percentile2039.3891
Maximum2081.1495
Range2095.642
Interquartile range (IQR)1186.5156

Descriptive statistics

Standard deviation700.80795
Coefficient of variation (CV)0.8913137
Kurtosis-1.0531555
Mean786.26408
Median Absolute Deviation (MAD)474.34175
Skewness0.60583664
Sum41084657
Variance491131.78
MonotonicityNot monotonic
2023-07-08T17:28:02.947763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.74000001 3
 
< 0.1%
-0.4876190156 3
 
< 0.1%
-1.024853528 2
 
< 0.1%
-1.960000038 2
 
< 0.1%
-0.5148275107 2
 
< 0.1%
-1.358824527 2
 
< 0.1%
0 2
 
< 0.1%
-0.6023215175 2
 
< 0.1%
-2.769398516 2
 
< 0.1%
-1.148625527 2
 
< 0.1%
Other values (52220) 52231
99.1%
(Missing) 451
 
0.9%
ValueCountFrequency (%)
-14.49252149 1
< 0.1%
-14.46196609 1
< 0.1%
-14.31465816 1
< 0.1%
-14.11140456 1
< 0.1%
-14.10410872 1
< 0.1%
-13.80459852 1
< 0.1%
-13.28981109 1
< 0.1%
-12.50698555 1
< 0.1%
-12.40530159 1
< 0.1%
-12.35464756 1
< 0.1%
ValueCountFrequency (%)
2081.149487 1
< 0.1%
2080.385461 1
< 0.1%
2078.979639 1
< 0.1%
2075.42146 1
< 0.1%
2075.299274 1
< 0.1%
2074.680377 1
< 0.1%
2074.671857 1
< 0.1%
2074.663867 1
< 0.1%
2074.248657 1
< 0.1%
2074.184174 1
< 0.1%

Wind direction (°)
Real number (ℝ)

Distinct52252
Distinct (%)100.0%
Missing452
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean198.26606
Minimum0.0088506615
Maximum359.97028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:03.042948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.0088506615
5-th percentile30.281706
Q1146.11415
median217.44011
Q3258.72396
95-th percentile327.23791
Maximum359.97028
Range359.96143
Interquartile range (IQR)112.60981

Descriptive statistics

Standard deviation90.886592
Coefficient of variation (CV)0.45840721
Kurtosis-0.63493577
Mean198.26606
Median Absolute Deviation (MAD)49.987017
Skewness-0.55044514
Sum10359798
Variance8260.3725
MonotonicityNot monotonic
2023-07-08T17:28:03.138251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
264.2650987 1
 
< 0.1%
31.26087436 1
 
< 0.1%
279.5289272 1
 
< 0.1%
209.9247615 1
 
< 0.1%
159.422624 1
 
< 0.1%
74.7489434 1
 
< 0.1%
25.2250473 1
 
< 0.1%
356.0874239 1
 
< 0.1%
344.4018338 1
 
< 0.1%
295.7011647 1
 
< 0.1%
Other values (52242) 52242
99.1%
(Missing) 452
 
0.9%
ValueCountFrequency (%)
0.008850661519 1
< 0.1%
0.0172743059 1
< 0.1%
0.04795168537 1
< 0.1%
0.05323209476 1
< 0.1%
0.06068829484 1
< 0.1%
0.06209702858 1
< 0.1%
0.10066375 1
< 0.1%
0.1018755274 1
< 0.1%
0.1049417314 1
< 0.1%
0.1213648434 1
< 0.1%
ValueCountFrequency (%)
359.9702789 1
< 0.1%
359.9647985 1
< 0.1%
359.9576589 1
< 0.1%
359.9132425 1
< 0.1%
359.9028128 1
< 0.1%
359.8957745 1
< 0.1%
359.8910482 1
< 0.1%
359.8854283 1
< 0.1%
359.8429117 1
< 0.1%
359.8369728 1
< 0.1%

Nacelle position (°)
Real number (ℝ)

Distinct14808
Distinct (%)28.3%
Missing452
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean198.16499
Minimum0.013192621
Maximum359.96844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:03.240103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.013192621
5-th percentile29.487252
Q1144.55023
median217.5574
Q3259.26505
95-th percentile326.1696
Maximum359.96844
Range359.95525
Interquartile range (IQR)114.71481

Descriptive statistics

Standard deviation91.406422
Coefficient of variation (CV)0.46126424
Kurtosis-0.64232089
Mean198.16499
Median Absolute Deviation (MAD)50.486908
Skewness-0.54604382
Sum10354517
Variance8355.1339
MonotonicityNot monotonic
2023-07-08T17:28:03.333631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210.9730225 194
 
0.4%
234.0213623 192
 
0.4%
260.3634338 145
 
0.3%
205.485199 144
 
0.3%
252.680481 121
 
0.2%
242.8023987 118
 
0.2%
311.9479675 114
 
0.2%
213.1681519 111
 
0.2%
223.0462341 111
 
0.2%
287.8025513 108
 
0.2%
Other values (14798) 50894
96.6%
(Missing) 452
 
0.9%
ValueCountFrequency (%)
0.01319262075 1
 
< 0.1%
0.02226442459 1
 
< 0.1%
0.03564797833 1
 
< 0.1%
0.2250601555 1
 
< 0.1%
0.2409045547 1
 
< 0.1%
0.2412719727 14
< 0.1%
0.2413623333 10
< 0.1%
0.2413635403 16
< 0.1%
0.2414245605 8
< 0.1%
0.2416331768 3
 
< 0.1%
ValueCountFrequency (%)
359.9684443 1
< 0.1%
359.9476391 1
< 0.1%
359.8356939 1
< 0.1%
359.6409432 1
< 0.1%
359.5866073 1
< 0.1%
359.5347401 1
< 0.1%
359.500335 1
< 0.1%
359.3939653 1
< 0.1%
359.1976066 1
< 0.1%
359.1936972 1
< 0.1%

blade_angle
Real number (ℝ)

MISSING  ZEROS 

Distinct20979
Distinct (%)40.3%
Missing705
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean6.0374117
Minimum0
Maximum92.723333
Zeros23179
Zeros (%)44.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:03.433952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.049000001
Q31.861496
95-th percentile44.990002
Maximum92.723333
Range92.723333
Interquartile range (IQR)1.861496

Descriptive statistics

Standard deviation16.207539
Coefficient of variation (CV)2.6845178
Kurtosis13.233399
Mean6.0374117
Median Absolute Deviation (MAD)0.049000001
Skewness3.5551042
Sum313939.37
Variance262.68432
MonotonicityNot monotonic
2023-07-08T17:28:03.525972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23179
44.0%
44.99000168 2856
 
5.4%
89.98999786 450
 
0.9%
0.02450000048 376
 
0.7%
1.49000001 295
 
0.6%
0.04900000095 183
 
0.3%
92.10666656 143
 
0.3%
0.02449974513 142
 
0.3%
0.07350000143 95
 
0.2%
0.4900000095 84
 
0.2%
Other values (20969) 24196
45.9%
(Missing) 705
 
1.3%
ValueCountFrequency (%)
0 23179
44.0%
0.0001666666622 8
 
< 0.1%
0.0001666666629 22
 
< 0.1%
0.0001754385918 2
 
< 0.1%
0.0001754385926 4
 
< 0.1%
0.0001851851803 1
 
< 0.1%
0.000185185181 2
 
< 0.1%
0.0002083333287 1
 
< 0.1%
0.0002564102497 1
 
< 0.1%
0.0003333333201 2
 
< 0.1%
ValueCountFrequency (%)
92.72333272 5
 
< 0.1%
92.71000163 2
 
< 0.1%
92.70333099 1
 
< 0.1%
92.69999949 1
 
< 0.1%
92.55666606 17
 
< 0.1%
92.48999786 71
0.1%
92.46272856 1
 
< 0.1%
92.37999725 33
0.1%
92.33399773 1
 
< 0.1%
92.20315873 1
 
< 0.1%

Rear bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37978
Distinct (%)73.0%
Missing705
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean65.781159
Minimum7.8300002
Maximum77.047501
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:03.619506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum7.8300002
5-th percentile43.945
Q165.555001
median69.1425
Q370.845003
95-th percentile72.874999
Maximum77.047501
Range69.217501
Interquartile range (IQR)5.2900021

Descriptive statistics

Standard deviation9.8915319
Coefficient of variation (CV)0.15037029
Kurtosis10.360971
Mean65.781159
Median Absolute Deviation (MAD)2.2075012
Skewness-2.9783469
Sum3420554.5
Variance97.842403
MonotonicityNot monotonic
2023-07-08T17:28:03.713136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 11
 
< 0.1%
70.9875 11
 
< 0.1%
69.25249977 10
 
< 0.1%
69.47750015 10
 
< 0.1%
69.99000015 10
 
< 0.1%
70.00999985 10
 
< 0.1%
69.91500015 10
 
< 0.1%
70.62250023 9
 
< 0.1%
69.42750015 9
 
< 0.1%
70.32249985 9
 
< 0.1%
Other values (37968) 51900
98.5%
(Missing) 705
 
1.3%
ValueCountFrequency (%)
7.830000186 1
 
< 0.1%
7.850000238 1
 
< 0.1%
7.852500176 1
 
< 0.1%
7.862500143 1
 
< 0.1%
7.863889111 1
 
< 0.1%
7.88500011 1
 
< 0.1%
7.887500119 1
 
< 0.1%
7.890000153 1
 
< 0.1%
7.900000095 5
< 0.1%
7.925000095 1
 
< 0.1%
ValueCountFrequency (%)
77.04750099 1
< 0.1%
76.88000145 1
< 0.1%
76.87941248 1
< 0.1%
76.84500046 1
< 0.1%
76.79500122 1
< 0.1%
76.77750092 1
< 0.1%
76.74374914 1
< 0.1%
76.73947304 1
< 0.1%
76.73249969 1
< 0.1%
76.64210269 1
< 0.1%

Rotor speed (RPM)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50731
Distinct (%)97.1%
Missing451
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean11.129825
Minimum0
Maximum15.31934
Zeros1339
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:03.814953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.55204251
Q18.6765568
median11.983043
Q314.938414
95-th percentile15.173469
Maximum15.31934
Range15.31934
Interquartile range (IQR)6.2618575

Descriptive statistics

Standard deviation4.1683102
Coefficient of variation (CV)0.37451715
Kurtosis0.95614464
Mean11.129825
Median Absolute Deviation (MAD)3.045111
Skewness-1.2064181
Sum581566.72
Variance17.37481
MonotonicityNot monotonic
2023-07-08T17:28:03.911710image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1339
 
2.5%
8.140000343 59
 
0.1%
0.01050000242 16
 
< 0.1%
15.15999985 14
 
< 0.1%
0.01200000197 9
 
< 0.1%
0.01150000188 8
 
< 0.1%
0.0110000018 7
 
< 0.1%
15.14999962 7
 
< 0.1%
15.14000034 5
 
< 0.1%
8.149999619 5
 
< 0.1%
Other values (50721) 50784
96.4%
(Missing) 451
 
0.9%
ValueCountFrequency (%)
0 1339
2.5%
0.004092000425 1
 
< 0.1%
0.004741001059 1
 
< 0.1%
0.00708631681 1
 
< 0.1%
0.01050000242 16
 
< 0.1%
0.01063700253 1
 
< 0.1%
0.0110000018 7
 
< 0.1%
0.01109900186 1
 
< 0.1%
0.01150000188 8
 
< 0.1%
0.01157894927 1
 
< 0.1%
ValueCountFrequency (%)
15.3193405 1
< 0.1%
15.3103939 1
< 0.1%
15.30540517 1
< 0.1%
15.30220641 1
< 0.1%
15.30144355 1
< 0.1%
15.29843697 1
< 0.1%
15.29797411 1
< 0.1%
15.29260477 1
< 0.1%
15.29143153 1
< 0.1%
15.28772995 1
< 0.1%

Generator RPM (RPM)
Real number (ℝ)

Distinct52215
Distinct (%)99.9%
Missing452
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean1320.9817
Minimum-290.68586
Maximum1816.8374
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size411.9 KiB
2023-07-08T17:28:04.134209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-290.68586
5-th percentile66.966249
Q11031.4021
median1422.5381
Q31771.2529
95-th percentile1799.0295
Maximum1816.8374
Range2107.5233
Interquartile range (IQR)739.85079

Descriptive statistics

Standard deviation493.26355
Coefficient of variation (CV)0.37340681
Kurtosis0.97074458
Mean1320.9817
Median Absolute Deviation (MAD)359.35643
Skewness-1.2117602
Sum69023934
Variance243308.93
MonotonicityNot monotonic
2023-07-08T17:28:04.221008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
970 4
 
< 0.1%
969.9799805 4
 
< 0.1%
970.0200195 4
 
< 0.1%
970.1300049 3
 
< 0.1%
969.9099731 3
 
< 0.1%
969.9500122 3
 
< 0.1%
970.1199951 3
 
< 0.1%
969.8200073 2
 
< 0.1%
968.3208961 2
 
< 0.1%
1801.180054 2
 
< 0.1%
Other values (52205) 52222
99.1%
(Missing) 452
 
0.9%
ValueCountFrequency (%)
-290.6858639 1
< 0.1%
-48.32098131 1
< 0.1%
0.3000000119 1
< 0.1%
0.400000006 1
< 0.1%
1.162419822 1
< 0.1%
1.268706513 1
< 0.1%
1.276168026 1
< 0.1%
1.280778559 1
< 0.1%
1.298408316 1
< 0.1%
1.309341839 1
< 0.1%
ValueCountFrequency (%)
1816.837423 1
< 0.1%
1815.25355 1
< 0.1%
1815.012503 1
< 0.1%
1814.984247 1
< 0.1%
1814.687044 1
< 0.1%
1813.427771 1
< 0.1%
1812.953762 1
< 0.1%
1812.6931 1
< 0.1%
1812.258162 1
< 0.1%
1812.256044 1
< 0.1%
Distinct38550
Distinct (%)74.1%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean11.636198
Minimum-1.685
Maximum34.867501
Zeros0
Zeros (%)0.0%
Negative147
Negative (%)0.3%
Memory size411.9 KiB
2023-07-08T17:28:04.313831image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-1.685
5-th percentile3.7148333
Q17.330625
median10.973509
Q315.410526
95-th percentile21.8
Maximum34.867501
Range36.552501
Interquartile range (IQR)8.079901

Descriptive statistics

Standard deviation5.6442506
Coefficient of variation (CV)0.48505968
Kurtosis0.099734991
Mean11.636198
Median Absolute Deviation (MAD)4.0010086
Skewness0.54545594
Sum605059.04
Variance31.857565
MonotonicityNot monotonic
2023-07-08T17:28:04.408326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 64
 
0.1%
9 51
 
0.1%
6.400000095 48
 
0.1%
9.600000381 48
 
0.1%
9.300000191 48
 
0.1%
10.30000019 48
 
0.1%
8.5 47
 
0.1%
7.900000095 46
 
0.1%
10.39999962 46
 
0.1%
6.599999905 45
 
0.1%
Other values (38540) 51507
97.7%
(Missing) 706
 
1.3%
ValueCountFrequency (%)
-1.685000044 1
< 0.1%
-1.610000032 1
< 0.1%
-1.600000024 1
< 0.1%
-1.597368441 1
< 0.1%
-1.595000023 1
< 0.1%
-1.590000021 1
< 0.1%
-1.54473685 1
< 0.1%
-1.520000005 1
< 0.1%
-1.515000004 1
< 0.1%
-1.454999989 1
< 0.1%
ValueCountFrequency (%)
34.86750145 1
< 0.1%
34.8475008 1
< 0.1%
34.83684339 1
< 0.1%
34.77222294 1
< 0.1%
34.7382357 1
< 0.1%
34.68157839 1
< 0.1%
34.44999924 1
< 0.1%
34.39999992 1
< 0.1%
34.28000031 1
< 0.1%
34.20789478 1
< 0.1%

Front bearing temperature (°C)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38029
Distinct (%)73.1%
Missing705
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean66.426196
Minimum8.1100003
Maximum77.032499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:04.508947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8.1100003
5-th percentile43.38975
Q164.9575
median70.910525
Q372.347499
95-th percentile73.960053
Maximum77.032499
Range68.922499
Interquartile range (IQR)7.3899992

Descriptive statistics

Standard deviation10.627331
Coefficient of variation (CV)0.15998704
Kurtosis7.5611961
Mean66.426196
Median Absolute Deviation (MAD)2.2144751
Skewness-2.5454232
Sum3454095.8
Variance112.94016
MonotonicityNot monotonic
2023-07-08T17:28:04.604862image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.199999809 22
 
< 0.1%
72.25250015 18
 
< 0.1%
8.5 17
 
< 0.1%
72.25 16
 
< 0.1%
10.30000019 12
 
< 0.1%
70.5 11
 
< 0.1%
72.24499969 11
 
< 0.1%
72.22499847 11
 
< 0.1%
9 11
 
< 0.1%
73.54499969 10
 
< 0.1%
Other values (38019) 51860
98.4%
(Missing) 705
 
1.3%
ValueCountFrequency (%)
8.110000277 1
 
< 0.1%
8.122500086 1
 
< 0.1%
8.164999914 1
 
< 0.1%
8.174999857 1
 
< 0.1%
8.189999819 1
 
< 0.1%
8.1974998 1
 
< 0.1%
8.199999809 22
< 0.1%
8.269999695 1
 
< 0.1%
8.274999571 1
 
< 0.1%
8.324999619 1
 
< 0.1%
ValueCountFrequency (%)
77.03249931 1
< 0.1%
76.96750069 1
< 0.1%
76.94500084 1
< 0.1%
76.89250107 1
< 0.1%
76.88000183 1
< 0.1%
76.86500206 1
< 0.1%
76.83529618 1
< 0.1%
76.80500069 1
< 0.1%
76.79737091 1
< 0.1%
76.77500038 1
< 0.1%

Tower Acceleration X (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51998
Distinct (%)100.0%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean50.747235
Minimum2.3693025
Maximum242.51919
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:04.706132image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.3693025
5-th percentile4.9485001
Q131.623683
median48.393595
Q366.732604
95-th percentile101.71096
Maximum242.51919
Range240.14989
Interquartile range (IQR)35.108921

Descriptive statistics

Standard deviation28.599393
Coefficient of variation (CV)0.56356554
Kurtosis1.0869773
Mean50.747235
Median Absolute Deviation (MAD)17.462791
Skewness0.71246841
Sum2638754.7
Variance817.92528
MonotonicityNot monotonic
2023-07-08T17:28:04.802731image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.91811764 1
 
< 0.1%
4.337898338 1
 
< 0.1%
3.917700006 1
 
< 0.1%
4.368833086 1
 
< 0.1%
5.071487099 1
 
< 0.1%
4.329751797 1
 
< 0.1%
3.79516542 1
 
< 0.1%
3.950432378 1
 
< 0.1%
3.924470395 1
 
< 0.1%
4.666754145 1
 
< 0.1%
Other values (51988) 51988
98.6%
(Missing) 706
 
1.3%
ValueCountFrequency (%)
2.369302452 1
< 0.1%
2.779530656 1
< 0.1%
2.890906648 1
< 0.1%
2.910751186 1
< 0.1%
2.99637979 1
< 0.1%
3.10644262 1
< 0.1%
3.107045209 1
< 0.1%
3.146139753 1
< 0.1%
3.151026384 1
< 0.1%
3.211763448 1
< 0.1%
ValueCountFrequency (%)
242.5191889 1
< 0.1%
230.8551922 1
< 0.1%
216.0818129 1
< 0.1%
216.034354 1
< 0.1%
211.0308624 1
< 0.1%
210.3717165 1
< 0.1%
207.8339676 1
< 0.1%
207.7572626 1
< 0.1%
207.7117599 1
< 0.1%
207.4807144 1
< 0.1%

Wind speed (m/s)
Real number (ℝ)

Distinct52150
Distinct (%)99.8%
Missing452
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean6.8287519
Minimum0.20908153
Maximum24.259069
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:04.895935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.20908153
5-th percentile2.3542847
Q14.623856
median6.5154258
Q38.7429275
95-th percentile12.222179
Maximum24.259069
Range24.049987
Interquartile range (IQR)4.1190715

Descriptive statistics

Standard deviation3.0695086
Coefficient of variation (CV)0.44949775
Kurtosis0.62774848
Mean6.8287519
Median Absolute Deviation (MAD)2.0419684
Skewness0.63493332
Sum356815.94
Variance9.4218831
MonotonicityNot monotonic
2023-07-08T17:28:04.996699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.839999914 4
 
< 0.1%
2.289999962 4
 
< 0.1%
2.309999943 3
 
< 0.1%
4.730000019 3
 
< 0.1%
5.889999866 3
 
< 0.1%
2.5 3
 
< 0.1%
2.420000076 2
 
< 0.1%
2.880000114 2
 
< 0.1%
3.319999933 2
 
< 0.1%
3.039999962 2
 
< 0.1%
Other values (52140) 52224
99.1%
(Missing) 452
 
0.9%
ValueCountFrequency (%)
0.2090815317 1
< 0.1%
0.2333094531 1
< 0.1%
0.2461876355 1
< 0.1%
0.2770688798 1
< 0.1%
0.2981252423 1
< 0.1%
0.3070875908 1
< 0.1%
0.3091126699 1
< 0.1%
0.3177563846 1
< 0.1%
0.3184876293 1
< 0.1%
0.3221054297 1
< 0.1%
ValueCountFrequency (%)
24.25906868 1
< 0.1%
23.85808134 1
< 0.1%
23.44286237 1
< 0.1%
22.92345388 1
< 0.1%
22.86704988 1
< 0.1%
22.59070831 1
< 0.1%
22.40182495 1
< 0.1%
22.25812111 1
< 0.1%
22.23009372 1
< 0.1%
22.20307912 1
< 0.1%

Tower Acceleration y (mm/ss)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct51995
Distinct (%)> 99.9%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean30.62265
Minimum2.8631144
Maximum244.46582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:05.093680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2.8631144
5-th percentile4.9704177
Q116.960869
median26.867559
Q339.12386
95-th percentile68.852735
Maximum244.46582
Range241.6027
Interquartile range (IQR)22.162991

Descriptive statistics

Standard deviation20.490536
Coefficient of variation (CV)0.66913007
Kurtosis6.8081566
Mean30.62265
Median Absolute Deviation (MAD)10.828039
Skewness1.8727456
Sum1592316.6
Variance419.86207
MonotonicityNot monotonic
2023-07-08T17:28:05.191381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58.37895613 2
 
< 0.1%
42.92986107 2
 
< 0.1%
3.920765647 2
 
< 0.1%
7.109801567 1
 
< 0.1%
4.099412507 1
 
< 0.1%
3.885485359 1
 
< 0.1%
4.131577426 1
 
< 0.1%
4.660023999 1
 
< 0.1%
4.422043455 1
 
< 0.1%
5.896889458 1
 
< 0.1%
Other values (51985) 51985
98.6%
(Missing) 706
 
1.3%
ValueCountFrequency (%)
2.863114429 1
< 0.1%
2.950375021 1
< 0.1%
2.990819591 1
< 0.1%
2.991935268 1
< 0.1%
3.034610155 1
< 0.1%
3.048525482 1
< 0.1%
3.078922164 1
< 0.1%
3.091524184 1
< 0.1%
3.097699007 1
< 0.1%
3.09876554 1
< 0.1%
ValueCountFrequency (%)
244.4658167 1
< 0.1%
235.6611402 1
< 0.1%
221.0891705 1
< 0.1%
219.2622841 1
< 0.1%
212.8712793 1
< 0.1%
209.8682934 1
< 0.1%
202.4948265 1
< 0.1%
197.0183801 1
< 0.1%
196.8038925 1
< 0.1%
195.6807035 1
< 0.1%
Distinct33
Distinct (%)0.1%
Missing706
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean527.43521
Minimum506
Maximum541
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.9 KiB
2023-07-08T17:28:05.281076image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum506
5-th percentile508
Q1525
median527
Q3535
95-th percentile538
Maximum541
Range35
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.9591402
Coefficient of variation (CV)0.016986238
Kurtosis0.27492678
Mean527.43521
Median Absolute Deviation (MAD)6
Skewness-1.0035746
Sum27425576
Variance80.266193
MonotonicityIncreasing
2023-07-08T17:28:05.364805image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
535 9208
17.5%
526 9047
17.2%
508 4404
8.4%
527 4108
 
7.8%
532 2894
 
5.5%
537 2807
 
5.3%
529 2759
 
5.2%
521 2421
 
4.6%
534 2034
 
3.9%
525 1952
 
3.7%
Other values (23) 10364
19.7%
ValueCountFrequency (%)
506 905
 
1.7%
507 347
 
0.7%
508 4404
8.4%
509 1
 
< 0.1%
510 628
 
1.2%
511 325
 
0.6%
512 12
 
< 0.1%
513 24
 
< 0.1%
516 1
 
< 0.1%
517 73
 
0.1%
ValueCountFrequency (%)
541 293
 
0.6%
540 557
 
1.1%
539 1562
 
3.0%
538 628
 
1.2%
537 2807
 
5.3%
536 945
 
1.8%
535 9208
17.5%
534 2034
 
3.9%
533 55
 
0.1%
532 2894
 
5.5%

Interactions

2023-07-08T17:28:00.630479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.554543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.725699image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.883530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.042186image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.242023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.413547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.589293image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.775987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.946020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.115465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.347722image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.469753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.715200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.636623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.809091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.966020image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.119430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.325188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.498143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.664476image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.859836image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.028634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.196615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.428191image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.551483image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.812583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.724933image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.898671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.058304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.207148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.418667image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.590112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.751226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.952851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.123016image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.284807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.517225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.640870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.907095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.813740image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.991266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.147518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.292711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.511120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.683785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.838184image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.045824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.217179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.373689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.607557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.729564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.993897image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.892827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.074140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.231574image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.371583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.596275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.767423image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.914255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.129098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.301342image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.454324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.686960image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.812627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.090055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:46.980622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.168643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.324824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.458784image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.689926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.862957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.004014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.226412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.393475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.544548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.776213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.903688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.187714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.070072image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.263302image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.422316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.550000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.786950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.957707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.091029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.325079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.490455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.638411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.869447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.002159image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.269935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.144455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.345678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.502808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.625224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.868300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.040733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.164165image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.404911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.570196image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.715409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.947299image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.087287image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.365439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.298598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.438352image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.595969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.713297image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.963636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.133875image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.250235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.497175image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.664435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.807073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.038194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.181602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.461746image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.387932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.532176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.689497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.803951image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.056553image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.229319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.339118image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.590971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.755411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.897381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.127273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.276385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.550502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.469138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.617711image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.775827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:50.885110image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.145233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.317061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.420004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.678677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.844051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.983222image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.211620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.362741image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.641366image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.553039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.705051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.862023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.073237image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.231719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.405477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.502059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.764789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:56.931091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.067665image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.293813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.451732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:01.728926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:47.637466image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:48.789439image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:49.948680image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:51.152947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:52.320555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:53.493131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:54.691073image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:55.851253image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:57.020642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:58.152431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:27:59.379198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-07-08T17:28:00.536041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-07-08T17:28:05.449822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Power (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
Power (kW)1.0000.0670.0640.0230.6580.9910.990-0.2060.8100.6670.9710.857-0.125
Wind direction (°)0.0671.0000.9240.0050.0390.0650.066-0.1000.059-0.0610.0930.0110.037
Nacelle position (°)0.0640.9241.0000.0030.0320.0610.063-0.1060.054-0.0660.0870.0070.027
blade_angle0.0230.0050.0031.000-0.3760.0050.0030.056-0.2360.1310.0660.132-0.065
Rear bearing temperature (°C)0.6580.0390.032-0.3761.0000.6510.6450.0490.8840.3730.6320.5080.023
Rotor speed (RPM)0.9910.0650.0610.0050.6511.0000.998-0.2050.8100.6560.9620.847-0.115
Generator RPM (RPM)0.9900.0660.0630.0030.6450.9981.000-0.2190.8060.6550.9610.847-0.120
Nacelle ambient temperature (°C)-0.206-0.100-0.1060.0560.049-0.205-0.2191.000-0.124-0.089-0.213-0.1490.170
Front bearing temperature (°C)0.8100.0590.054-0.2360.8840.8100.806-0.1241.0000.4840.7820.663-0.047
Tower Acceleration X (mm/ss)0.667-0.061-0.0660.1310.3730.6560.655-0.0890.4841.0000.6360.856-0.131
Wind speed (m/s)0.9710.0930.0870.0660.6320.9620.961-0.2130.7820.6361.0000.846-0.100
Tower Acceleration y (mm/ss)0.8570.0110.0070.1320.5080.8470.847-0.1490.6630.8560.8461.000-0.148
Metal particle count counter-0.1250.0370.027-0.0650.023-0.115-0.1200.170-0.047-0.131-0.100-0.1481.000

Missing values

2023-07-08T17:28:01.868065image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T17:28:02.183595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-07-08T17:28:02.427092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
02020-01-01 00:00:00386.335927113.313270101.2167590.00000068.79499910.6860661269.5325456.52000068.86999933.9181185.37101121.755648506.0
12020-01-01 00:10:00276.166611114.925915101.2167590.00000068.3250009.7182671155.3688726.59500068.75000049.6365424.77485721.897640506.0
22020-01-01 00:20:00243.963609117.427182101.2167590.00000067.2700019.3394371111.3899596.60000067.61750159.4323994.81987126.757732506.0
32020-01-01 00:30:00113.998529117.950283101.2167590.29649165.4600008.252814982.2436446.51500065.28500164.1208603.56773820.242523506.0
42020-01-01 00:40:00150.627731115.878294101.2167590.19699764.7474998.5704021019.6784276.44000063.61500160.8440583.97529625.919929506.0
52020-01-01 00:50:00230.820182126.440753110.8165450.00000066.4894749.2409811098.1125526.55526365.81052776.4845544.39513526.542848506.0
62020-01-01 01:00:00229.265832129.845291119.8752290.00000066.5225009.1694711091.3595776.58000066.16250086.2145244.49905640.442194506.0
72020-01-01 01:10:00228.604192132.009559119.8752290.00000066.9350009.1607861089.0772486.45000066.805001112.1509584.47418239.533775506.0
82020-01-01 01:20:00246.251331128.608893119.8752290.00000066.9150019.3633161113.7455886.42000066.870000110.7705104.58789632.866978506.0
92020-01-01 01:30:00138.829758134.846493119.8752290.18749865.3425018.403713999.8567886.59500065.21500194.3389833.63164935.803017506.0
# Date and timePower (kW)Wind direction (°)Nacelle position (°)blade_angleRear bearing temperature (°C)Rotor speed (RPM)Generator RPM (RPM)Nacelle ambient temperature (°C)Front bearing temperature (°C)Tower Acceleration X (mm/ss)Wind speed (m/s)Tower Acceleration y (mm/ss)Metal particle count counter
526942020-12-31 22:20:00-2.638691311.497181314.14303689.98999817.6525000.02.2027521.390019.8400006.3559206.2426818.358898541.0
526952020-12-31 22:30:00-2.692041309.822020314.14303689.98999817.4725000.02.1052501.400019.6825016.2077266.04381911.102482541.0
526962020-12-31 22:40:00-3.603259301.837791314.14303689.98999817.2650000.01.7162541.400019.5700005.5220765.8019817.254235541.0
526972020-12-31 22:50:00-2.601346304.866016314.14303689.98999817.1375010.01.6487421.460019.3374994.2325635.4419255.089394541.0
526982020-12-31 23:00:00-2.793406303.504024314.14303689.98999816.8800000.01.7198101.550019.2100004.7815545.3961194.803615541.0
526992020-12-31 23:10:00-3.478954303.276535314.14303689.98999816.7575000.01.5695731.600019.0000004.3499955.1447374.657638541.0
527002020-12-31 23:20:00-3.365318303.620920314.14303689.98999816.5650000.01.6997871.600018.8150003.5768324.9794004.380813541.0
527012020-12-31 23:30:00-3.286360299.232986314.14303689.98999816.3350000.01.6966701.600018.6300004.8382505.0882815.966883541.0
527022020-12-31 23:40:00-4.288273297.566402314.14303689.98999816.1750010.01.7507011.600018.4475004.6767244.8718315.034439541.0
527032020-12-31 23:50:00-2.928382304.999392314.14303689.98999816.0850000.01.7006001.442518.2700004.2161715.5833945.338068541.0